In statistical learning theory, expressive capacity (i.e., hypothesis space complexity) is used to bound generalization error [1]}. Specifically, let \(\mathcal {F}\)  be the set of functions representable by a certain model structure. Let \(f_{A(D)}\)  be a function \(f\in \mathcal {F}\)  learned by algorithm \(A\)  on training dataset \(D\) . Let \(E_D(f_{A(D)})\)  be the emperical error of \(f_{A(D)}\)  and \(E(f_{A(D)})\)  the generalization error of \(f_{A(D)}\) . The gap between generalization error and emperical error is bounded by
\(E(f_{A(D)}) - E_D(f_{A(D)}) \le \sup _{f \in \mathcal {F}} \lbrace E(f) - E_D(f)\rbrace \) 
